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Data, Analytics & AI
Advanced AI & Analytics
Forecasting, segmentation, anomaly detection.
Machine-learning models wired into operational workflows — instrumented, observable, and continuously verified against business KPIs.
The case
Most analytics
ends as a chart, not a decision.
Forecasting models that aren't embedded in the planning workflow are slide-deck art. Segmentation that the CRM team doesn't trust gets ignored. Anomaly scoring that floods a queue without context becomes noise.
We design analytics for the decision they support — not the dashboard they decorate. Every model is wired to a workflow, instrumented against a business KPI, and continuously verified after launch.
What we build
Models
with operational wiring.
Forecasting & demand sensing
Hierarchical forecasting that accounts for promotions, seasonality, and external signals — and writes back into the planning system, not a one-off dashboard.
Customer segmentation
Behavioral clusters and lifetime-value models powering personalization, lifecycle marketing, and CRM workflows — refreshed on cadence, not as a project.
Anomaly detection
Real-time anomaly scoring for fraud, operational risk, and quality in industrial telemetry — context-rich enough that the operator trusts the queue.
Decision intelligence
Optimization and reinforcement-learning systems that translate predictions into actions — pricing, scheduling, routing, allocation.
Causal & uplift modeling
Causal-inference and uplift models that answer “how much did this actually move the metric” — finance-grade, not vanity-grade.
Self-serve analytics enablement
Notebook patterns, semantic-layer hooks, and analyst guardrails that let your data team scale without losing rigor.
How we engage
Four phases —
from business question to compounding model.
Each phase ends in an artefact your operations team uses the next quarter — not a deck. We refuse to ship a model without a defined decision, an operator, and a measurable KPI tied to it.
Frame
Weeks 1–2
Translate the business question into a model spec. Define the decision the model supports, the operator who'll act on it, and the KPI we'll measure against in production.
Prototype
Weeks 2–6
A working model on real data — accuracy, latency, and explainability profiled against the production constraints. Stakeholder check-ins weekly so the spec evolves with the evidence.
Productionize
Weeks 6–12
MLOps pipeline, monitoring, drift surveillance, eval-as-code. The model earns a registry entry, an SLO, and a runbook before it sees a single real decision.
Operate
Ongoing
Continuous evaluation against the business KPI. Drift surveillance, retraining cadence, and operator feedback loops live on the same backlog as the rest of the product.
Reference architecture
Models meet workflows
through a thin, observable seam.
The architecture that survives — not the most-recommended one. Each layer owns a clear responsibility and inherits the governance posture of the lakehouse below it.
Data layer
Layer 01Governed, AI-ready data sourced from the lakehouse, feature-store-aware, and instrumented for drift before the first model trains on it.
Modeling layer
Layer 02Where models are trained, registered, and version-controlled. Reproducibility is a first-class concern — not an afterthought retrofitted under audit pressure.
Decisioning layer
Layer 03The thin seam between model output and operational workflow. Where score becomes decision, with explanations, thresholds, and human review built in.
Workflow layer
Layer 04The system the human operator actually works in. Models that don't reach this layer are slide-deck art — every engagement we run lands here.
Observability plane
Layer 05Continuous evidence — model and decision, instrumented as one. Drift, performance, and KPI movement tracked against the business question that started the work.
Stacks we work with
The toolkit for
models that actually ship.
Most engagements span four to six of the categories below. The choice tracks the lakehouse you already run on — we resist re-platforming for tooling fashion alone, and we benchmark against simple baselines before reaching for the deep-learning hammer.
Modeling frameworks
Where models are trained. Choice driven by team familiarity, the deployment target, and the maturity of the model class — gradient-boosted trees still win more often than the LinkedIn discourse implies.
Time-series & forecasting
Hierarchical, intermittent-demand-aware, and external-signal-friendly. We benchmark against simple baselines on every engagement — most published benchmarks quietly omit the comparison that matters.
Optimization & decisioning
Where predictions become decisions. Linear / mixed-integer for constrained problems; reinforcement learning where the reward signal is genuinely measurable and the explore-exploit cost is acceptable.
Feature & registry
The components most enterprises buy last and need first. Feature reuse and disciplined versioning are what separate a working model from a working capability that compounds.
Serving
Where the model meets the request. Picked against latency budget, batch vs. realtime, and how strict the rollback path needs to be in your operational context.
Monitoring & evaluation
Drift, performance, and decision quality in production. The component that determines whether the model is genuinely working twelve months in — or quietly silenced and worked around.
Outcomes we engineer for
What models
are measured on.
We refuse to ship a model without a measurable business KPI tied to it. These are typical lifts post-deployment, observed in production over the first two quarters.
+7%
Revenue lift
Typical first-season lift from dynamic-pricing and demand-sensing rollouts in seasonal businesses.
−50%
Fraud false-positives
Reduction in false positives at parity recall when a legacy rules engine is augmented with ML scoring.
−30%
Forecast error
Reduction in MAPE across hierarchical forecasts when promotions and external signals are properly modeled.
Days, not weeks
Anomaly time-to-action
Median time from anomaly to operator action when scoring is wired to the workflow, not a dashboard.
Where this applies
Anywhere a decision
rides on a forecast.
If your business runs on a forecast, a segmentation, or an anomaly queue — the engineering posture is the same regardless of vertical.
- Retail & Luxury
- Consumer Goods & D2C
- Hospitality & Travel
- Banking & Capital Markets
- Insurance & Reinsurance
- Healthcare Providers & Payers
- Pharma & Life Sciences
- Manufacturing
- Energy & Utilities
- Telecom & Media
- Logistics & Mobility
- B2B SaaS
- Public Sector
- Higher Education
Common questions
Before we model,
what teams ask.
How do you measure whether a model is actually working?
Every engagement starts by defining the decision the model supports, the business KPI it must move, and the post-deployment evaluation cadence. We refuse to ship without all three.
What if our data isn't clean enough yet?
It rarely is. We treat the data quality work as part of the engagement scope, not a precondition. We've shipped working models with imperfect data — what matters is that the imperfection is bounded and observable.
Will the models keep working after you leave?
Yes — that's the entire point. We deliver MLOps, monitoring, and runbooks alongside the model. Most engagements include a 60–90 day stabilization period before full handoff.
Related in Data, Analytics & AI
Adjacent capabilities
in this practice.
Data Strategy & Consulting
Capability audits, target-state architectures, and a sequenced investment thesis tied to operating KPIs.
Data Lakehouse & Warehouse
Lakehouse on Databricks, Snowflake, or BigQuery — engineered for governed scale and downstream AI.
SAP Data & Analytics
Datasphere, BTP, and SAP Analytics Cloud — engineered into a federated data fabric across SAP and non-SAP.
Start the conversation
From dashboards
to decisions that move the KPI.
Tell us the decision you want better. We'll work back from there.